import pandas as pd
import numpy as np
from PlateAnalysis import PlateDataset
import matplotlib.pyplot as plt
import re

ConcMap_Plate1 = dict(zip(range(1,25),[7.58]*6 + \
                       [15.161]*6 + \
                       [7.58]*6 + \
                       [15.161]*6))

ConcMap_Plate2 = dict(zip(range(1,25),[8.781889]*6 + \
                       [16.051088]*6 + \
                       [8.781889]*6 + \
                       [16.051088]*6))

VolMap = dict(zip(range(1,25),[20]*12 + \
                       [40]*12 ))

def PlotAndGetMerics(path,ConcMap, VolMap,triton):
    Plate = PlateDataset(path)

    metrics = pd.DataFrame([],columns = ['Percent Triton','Vol','[Protein]','Kd',\
                                         'Vmax','R_sq','Noise','Degree Shift'])

    for j in range(1,25):

        vol = VolMap[j]
        protConc = ConcMap[j]

        print('Triton: {}\t Working Volume: {}\t Protein Conc: {}'.format(triton,vol,protConc))

        NormalizedTraces, DifferenceSpec, DiffDiff, DegShift= Plate.AnalysisPipeline_1(j)
        Plate.PlotTrace(NormalizedTraces)
        Plate.PlotTrace(DifferenceSpec)
        K,percentDMSO = 3, 5
        concs = Plate.CalculateCompoundConcs(K,vol,percentDMSO)
        km, vmax, loss = Plate.FitMichaelisMenten(concs, DiffDiff)
        Plate.PlotMichaelesMenten(DiffDiff,concs,\
                              km, vmax, loss,\
                              'Plate: {}'.format(Plate.plate_data))
        noise = Plate.Noise(NormalizedTraces)

        temp = pd.DataFrame([[triton,vol,protConc,km,vmax,1-loss, noise,DegShift]],\
                                      columns = ['Percent Triton','Vol','[Protein]',\
                                                 'Kd','Vmax','R_sq','Noise','Degree Shift'])
        metrics = metrics.append(temp,sort = True)

    metrics.reset_index(inplace = True,drop=True)
    return metrics

KPiMetrics = PlotAndGetMerics('20191217_Kpi.CSV',ConcMap_Plate1, VolMap,triton=0)
TritonMetrics = PlotAndGetMerics('20191217_KpiTriton.CSV',ConcMap_Plate2, VolMap,triton=1)

output = KPiMetrics.append(TritonMetrics)
output.reset_index(inplace = True,drop=True)
output.to_csv('20191217_PlateMetrics.csv')

Triton: 0	 Working Volume: 20	 Protein Conc: 7.58

100%|██████████| 1000/1000 [00:00<00:00, 2142.94it/s]

Triton: 0	 Working Volume: 20	 Protein Conc: 7.58

100%|██████████| 1000/1000 [00:00<00:00, 2279.46it/s]

Triton: 0	 Working Volume: 20	 Protein Conc: 7.58

100%|██████████| 1000/1000 [00:00<00:00, 2094.89it/s]

Triton: 0	 Working Volume: 20	 Protein Conc: 7.58

100%|██████████| 1000/1000 [00:00<00:00, 1793.99it/s]

Triton: 0	 Working Volume: 20	 Protein Conc: 7.58

100%|██████████| 1000/1000 [00:00<00:00, 2126.12it/s]

output

import seaborn as sns

g = sns.pairplot(output.dropna())
plt.show()

from PathLength import PlatePathlength

KPi_PlatePathLength = PlatePathlength('20191217_pathlength_1.CSV',0.1)
Triton_PlatePathLength = PlatePathlength('20191217_pathlength_2.CSV',0.1)

KPi_PlatePathLength.PlotPlatePathlenghs()
Triton_PlatePathLength.PlotPlatePathlenghs()

	Degree Shift	Kd	Noise	Percent Triton	R_sq	Vmax	Vol	[Protein]
0	0.053893	35.181252	0.002478	0	0.986295	0.048889	20	7.580
1	0.141816	26.648445	0.002868	0	0.988172	0.042622	20	7.580
2	0.687385	40.671169	0.002401	0	0.984933	0.046520	20	7.580
3	0.378226	32.401878	0.001664	0	0.986372	0.044867	20	7.580
4	0.095330	36.731842	0.004029	0	0.985891	0.048033	20	7.580
5	0.041183	28.304813	0.000780	0	0.987835	0.043561	20	7.580
6	1.146147	44.052696	0.004773	0	0.983936	0.061841	20	15.161
7	0.988525	38.042732	0.006426	0	0.985202	0.058882	20	15.161
8	0.725714	47.100773	0.004664	0	0.983296	0.065113	20	15.161
9	6.796467	29.174292	0.007526	0	0.986320	0.054498	20	15.161
10	0.606433	48.305756	0.002476	0	0.982820	0.065121	20	15.161
11	0.406740	31.939753	0.000552	0	0.986592	0.061577	20	15.161
12	37.373332	33.859905	0.008629	0	0.984837	0.071640	40	7.580
13	53.105473	30.294701	0.008277	0	0.986816	0.082606	40	7.580
14	0.425360	39.068981	0.007535	0	0.985289	0.099862	40	7.580
15	0.822582	36.524937	0.011298	0	0.985626	0.090260	40	7.580
16	0.169426	37.749489	0.002936	0	0.985674	0.100008	40	7.580
17	0.840988	35.815987	0.011929	0	0.985956	0.094761	40	7.580
18	0.703400	46.252796	0.003965	0	0.983446	0.138271	40	15.161
19	1.422770	37.669594	0.019740	0	0.985562	0.115076	40	15.161
20	0.157239	46.168076	0.002759	0	0.983417	0.141027	40	15.161
21	0.906201	41.108604	0.010949	0	0.984719	0.129187	40	15.161
22	0.298634	45.116188	0.001927	0	0.983716	0.139201	40	15.161
23	1.364912	39.394058	0.010267	0	0.985232	0.125428	40	15.161
24	0.040912	64.956848	0.006512	1	0.975304	0.046469	20	7.580
25	19.477697	57.979454	0.001941	1	0.977858	0.032104	20	7.580
26	0.074219	64.043594	0.001928	1	0.975811	0.042776	20	7.580
27	0.545350	50.300808	0.001923	1	0.980644	0.033484	20	7.580
28	0.151554	66.824348	0.002075	1	0.974496	0.040538	20	7.580
29	1.341194	55.549305	0.003440	1	0.978284	0.036560	20	7.580
30	4.154818	87.505753	0.012627	1	0.961555	0.117336	20	15.161
31	2.144766	62.301949	0.010350	1	0.974610	0.075637	20	15.161
32	1.047660	78.689346	0.006846	1	0.967535	0.107072	20	15.161
33	105.646114	53.223331	0.009548	1	0.975925	0.080818	20	15.161
34	1.238241	75.658134	0.007935	1	0.969333	0.111701	20	15.161
35	47.600508	52.889118	0.014459	1	0.976712	0.066880	20	15.161
36	0.090038	55.568256	0.003556	1	0.979589	0.050309	40	7.580
37	1.098151	43.557678	0.008495	1	0.982936	0.047178	40	7.580
38	0.244774	56.585217	0.003317	1	0.979241	0.048932	40	7.580
39	1.957037	51.296253	0.009354	1	0.980393	0.045646	40	7.580
40	0.121938	55.399578	0.005670	1	0.979459	0.052411	40	7.580
41	9.667485	50.459568	0.006497	1	0.980873	0.043229	40	7.580
42	0.332498	65.218826	0.003616	1	0.974949	0.133547	40	15.161
43	5.603914	43.559280	0.011827	1	0.981176	0.099589	40	15.161
44	0.453134	67.206566	0.002957	1	0.974263	0.135396	40	15.161
45	9.181211	43.670204	0.013744	1	0.981206	0.102481	40	15.161
46	0.162697	67.814102	0.002314	1	0.973911	0.134242	40	15.161
47	5.284549	42.910038	0.014445	1	0.981255	0.101502	40	15.161